How AI improves telecom network capacity planning?

Once implemented and configured, network AI technology may automate network capacity planning while considering the organization’s financial and risk appetite. AI can assess many data points in real-time or near-real-time, which is crucial as companies shift to virtualized network overlays across their data centers, cloud environments, and WAN. AI may also be used to analyze network traffic patterns in various ways, allowing businesses to acquire a better understanding of what’s happening on the network and the overall network load. This information is helpful for capacity planning, both short and long-term network capacity planning. An increase in data consumption necessitates a network efficiency-focused approach, with a primary focus on lowering the total cost of ownership. So, how can communication service providers use AI to better plan network capacity and enhance customer experience?

What makes network capacity planning so complex? 

Network capacity planning aims to guarantee that enough bandwidth is allocated, allowing network SLA targets like delay, jitter, loss, and availability to be met consistently. It’s a time-consuming, error-prone task with significant cost ramifications or network optimization. Until recently, static, historical, after-the-fact reports were the only way to get the network data needed for insightful capacity planning. Thanks to artificial intelligence, this position is fast changing. New tools such as AI in telecom assist in estimating network needs and in provisioning the difficult task of network capacity.  Below are the pointers how AI can help in network capacity planning:

1. AI enhances traditional network capacity planning

IT can drive new and smarter predictive insights to increase network capacity planning accuracy by combining advanced data science and cognitive technology such as AI and machine learning. It enables businesses to liberate data to make more agile decisions, increase operational wisdom, prevent downtime, and improve user experience. AI simulates various performance situations and relates network performance to application performance to identify how different performance conditions affect applications. It uses AI-driven machine learning to improve network performance; a network controller can learn from its previous experiences while improving the network with artificial intelligence in the telecom sector.

2. AI Enables proactive actions 

Advanced machine-learning algorithms can provide precise demand estimates for each node in the network and detect intertemporal patterns/trends in network traffic and utilization using large-scale and extremely granular network data as inputs. Improved traffic and demand forecasting will allow for a more accurate estimate of network capacity needs, reducing the need for resource over-provisioning. Organizations can take proactive measures to ensure network performance by detecting and discovering intertemporal trends or changes in network traffic early. Sophisticated predictive models can be paired with optimization or simulation approaches to automatically develop the ideal network topology or structures, the accompanying capacity, and resource plans. These strategies can then be adjusted to the exact performance measures that matter most to the company.

3. From slow and manual to fast, scalable and flexible 

In a 1,800-site LTE network, AI use cases in telecom facilitate planning and automate sophisticated data gathering, aggregation, forecasting, dimensioning triggers, and prioritization to reduce real-time planning scenario analysis from weeks to five minutes. The planning strategy phase is the first step in any capacity planning process. It is where network performance, user experience expectations, market segmentation, and business strategy are examined to create the basis of the capacity planning phase’s input. When a more sophisticated approach is not available, spreadsheets are frequently utilized in the capacity planning phase. The pressure is on planning engineers to produce a spreadsheet that performs traffic forecasting and performance prediction using their best knowledge.

4. Cognitive Planning

By combining different planning inputs into the application, operators can significantly examine multiple scenarios to reduce turnaround time. It is possible to partition the network into numerous market areas, define thresholds on more than 20 KPIs and dimensions, and flexibly design spectrum distribution by fully utilizing the flexible input. Cognitive planning enables quick exploration of multiple what-if scenarios that balance different capacity expansion TCO against performance gains, transforms a seasonal activity into something that can be triggered at any time, and is critical in determining the best investment strategy for the future.

5. From NOCs to SOCs 

Other products aid CSPs in converting network operation centers (NOCs) to service operation centers (SOCs). A SOC employs analytics and AI to deliver closed-loop automation, whereas a NOC supervises, monitors, and maintains a telecommunications network. CSPs may now detect, diagnose, and recover from service-impacting issues without the need for human intervention.

AI/ML interventions and how it improves network capacity planning

AI assists CSPs in developing self-optimizing networks that maximize network quality based on traffic and service KPI data by area and time zone. These AI apps employ powerful algorithms to identify data trends to detect and anticipate network anomalies and proactively resolve issues before affecting customers. The goal is to use AI/ML to build a Self-Organized Network (SON), which will allow for closed-loop network management with self-planning, self-configuration, self-optimization, and self-healing.

• Conduct a network audit by examining key performance indicators (KPIs) for the network.
• Automated benchmarking by optimum correlation of network performance KPIs and parameter values.
• Maintaining an efficient network by auto-tuning configuration parameters when KPIs exceed a particular threshold.
• Antenna setup that is automated to solve coverage issues.

Has your organization adopted AI to improve network capacity planning? If yes, how is your organization doing that? Do let us know in the comment section.

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